Yun Wang
Senior Research Scientist, IPAC, Caltech, and
Professor of Physics & Astronomy, Univ. of Oklahoma
Fellow of the American Physical Society (elected 2012)
Regents' Award for Superior Research, Univ. of Oklahoma (2006)
B.S. 1985 Tsinghua Univ., P.R. China
M.S. 1987 CarnegieMellon
Ph.D. 1991 CarnegieMellon
Past Positions:
Postdoctoral Positions at
Univ. of Florida,
Fermilab Astrophysics Center,
Princeton University;
Visiting Assistant Professor at
Univ. of Notre Dame.


RESEARCH DESCRIPTION
My research has focused on extracting fundamental physics from cosmological
data, in particular, probing dark energy and early universe physics
using supernovae (SNe), cosmic microwave background anisotropy
(CMB), and cosmic large scale structure data.
I. Probing Dark Energy.
Our Universe has been observed to be undergoing accelerated expansion today. The unknown reason for this cosmic acceleration is referred to as "dark energy". At present, we do not know whether it is a new energy component of the Universe with negative pressure, or a modification of Einstein's theory of gravity (i.e., general relativity). Solving the mystery of the nature of dark energy is the most important problem in cosmology today. Dark energy can be probed using various techniques, most notably, using Type la supernovae (SNe la) as cosmological standard candles. I have done fundamental work in the use of supernovae to probe dark energy. My work has ranged from survey strategy, optimal data analysis, to the modeling of weak lensing effects. In the last several years, I have focused on using galaxy redshift surveys to probe dark energy and test gravity theories.
Here is some of the work that I have done:
Optimization of Dark Energy Projects:
I did the feasibility study of a deep supernova survey
on a dedicated telescope in 1998; the number of supernovae expected
from such a survey surprised and encouraged many observers.
In 2001, I studied the relative merits of a very deep
survey of supernovae as compared to shallower widefield surveys
in placing constraints on dark energy. Based on this, I developed
a mission concept for the NASADOE Joint Dark Energy Mission (JDEM),
Joint Efficient Darkenergy
Investigation (JEDI), in collaboration
with Arlin Crotts (Columbia) and Peter Garnavich (Notre Dame).
JEDI has significantly impacted the design of
space missions to probe dark energy (including Euclid and WFIRST).
Extracting Dark Energy Constraints from Data:
In a series of papers starting in 2001, I have shown the value in analyzing
the dark energy density instead of (or in addition to) its equation of state.
This is because the dark energy density is more closely related to observables
(hence better constrained), and can also probe a greater range of dark energy
models than the dark energy equation of state. I have also demonstrated
the importance of making modelindependent parametrizations of dark
energy, and improving the robustness of the analysis by imposing priors
from complementary observational data (such as cosmic microwave background
and galaxy clustering) in a consistent manner (20012004).
I have also worked on using weak lensing (2003), Lymanalpha forest (2003), and
galaxy clustering (2004) as complementary probes of dark energy.
Wang & Tegmark (2004) establishes a consistent and robust theoretical
framework for extracting dark energy constraints from data.
Wang & Tegmark (2005) presents a new robust supernova data analysis
technique that minimizes systematic errors and shows the potential of
JEDI in measuring dark energy density. Wang (2009) derives
an accurate measurement of
dark energy density as a function of cosmic time using current data.
Weak Lensing Systematic of Supernova Cosmology:
I have shown how the
weak lensing effect of supernovae can be analytically modeled by a universal
probability density function derived from the matter power spectrum (2002). I
set up a framework for removing or minimizing the effect of weak lensing
of supernovae on cosmological constraints by fluxaveraging (I have made
my fluxaveraging code available to the public on my website) (2000,2004). Most
recently, I have shown that weak lensing effects may have already begun to set in
and must be dealt with in deriving robust constraints on dark energy (2005).
Probing Dark Energy and Testing Gravity with Galaxy Redshift Surveys:
Modified gravity models provide an alternative to dark energy in
explaining cosmic acceleration as caused by modification of Einstein's
theory of gravity.
A given measurement of cosmic expansion history H(z) can be fit
by either dark energy or modified gravity models.
But for given H(z), dark energy and modified gravity models
predict different growth rates for cosmic large scale structure
f_g(z). Wang (2008) shows that a feasible and relatively modest galaxy redshift
survey covering >10,000 square degrees and the redshift range of 0.5 to 2
can rule out a broad class of modified gravity models.
Wang et al. (2010) demonstrates that assuming that gravity is
not modified, the dark energy constraints are significantly tightened
when the growth rate information is used together with H(z).
II. Other Research.
The primordial power spectrum P_in(k) opens an window into early
universe physics. I pioneered the modelindependent measurement of
P_in(k) from data in a paper published in ApJ in 1999.
Wang & Mathews (2002) made the first measurement of
the primordial power spectrum from CMB data.
Pia Mukherjee and I have developed new
and powerful techniques using wavelets to extract the primordial power
spectrum (20032005). Our study of simulated data show that
our technique can reliably recover features in the primordial power spectrum.
The application of our technique to the CMB data from WMAP
has revealed possible features that may be related to unusual
inflationary physics. It will be interesting to see what results we
will obtain when the four year WMAP data and Planck data become available.
Pia Mukherjee and I have studied primordial nonGaussianity
using the WMAP data (2004).
I have also worked on prospects for constraining cosmological models
with the extragalactic CMB temperature (2001),
constraints on extra dimensions from cosmological and terrestrial
measurements (2001), and
constraints on neutrino degeneracy from the cosmic microwave background
and primordial nucleosynthesis (2002).
III. Future Research Plan.
I plan to continue playing a leading role in dark energy research.
In the next five to ten years, I will focus on studying various research topics
critical to the ground and spacebased galaxy redshift surveys
including BOSS, Euclid, and WFIRST, and other dark energy projects
ALPACA and LSST.
In the next ten to fifteen years, I will continuously examine the observational
constraints on dark energy from observational data, and compare them
with viable new models, in an effort to solve the mystery of dark energy.
Selected Publications:
Yun Wang, et al.,
Designing a spacebased galaxy redshift survey to probe dark energy,
MNRAS, 409, 737 (2010)
Yun Wang,
Distance Measurements from Supernovae and Dark Energy Constraints,
PRD 80, 123525 (2009)
Yun Wang,
Differentiating dark energy and modified gravity with galaxy redshift surveys,
JCAP 05 (2008) 021
Yun Wang, and Pia Mukherjee,
Observational Constraints on Dark Energy and Cosmic Curvature,
PRD, 76, 103533 (2007)
Yun Wang, and Pia Mukherjee,
Robust Dark Energy Constraints from Supernovae, Galaxy Clustering, and
ThreeYear Wilkinson Microwave Anisotropy Probe Observations,
ApJ, 650, 1 (2006)
Yun Wang, and Max Tegmark,
Uncorrelated Measurements of the Cosmic Expansion History and
Dark Energy from Supernovae,
Phys. Rev. D 71, 103513 (2005)
Yun Wang, and Max Tegmark,
New Dark Energy Constraints from Supernovae, Microwave Background
and Galaxy Clustering,
Phys. Rev. Lett., 92, 241302 (2004)
Pia Mukherjee, and Yun Wang,
ModelIndependent Reconstruction of the Primordial Power Spectrum from
WMAP Data,
ApJ, 599, 1 (2003)
Yun Wang, Daniel E. Holz, and Dipak Munshi,
A Universal Probability Distribution Function for Weaklensing Amplification,
ApJ Letter, 572, L15L18 (2002)
Yun Wang and Peter Garnavich,
Measuring TimeDependence of Dark Energy Density from Type Ia
Supernova Data, ApJ 552, 445 (2001).
Yun Wang,
Fluxaveraging Analysis of Type Ia Supernova Data'',
ApJ, 536, 531 (2000).
Yun Wang,
Supernova Pencil Beam Survey,
ApJ, 531, 676 (2000) [astroph/9806185].
Yun Wang, David N. Spergel, and Michael Strauss,
Cosmology in the Next Millennium: Combining MAP and SDSS Data to Constrain
Inflationary Models,
ApJ, 510, 20 (1999)